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1 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
A Critical Review of Risk Factors of the Gaming Industry in Macau
Prof. Xing Wen Xiang
Dr. L. C. Koo
Dr. Eva Y.W. Khong
Abstract:
Macau has a thriving gaming industry which is crucial to the economic success currently. However, the
global economy is inevitably affected by the European debt crisis and a fragile U.S. economy. In addition,
with the Mainland economy showing signs of a slowdown, in the face of uncertainties around in Macau, it
is a point in time to critically review the factors that may adversely affect the long term, healthy, and
harmonious development of the entire gaming industry. We need to be vigilant in peace time through
developing and maintaining an effective risk management mechanism to ensure the sustained
development of the gaming industry. The operational definition of risk in the industry is explored in this
study. This research adopts the Hierarchical Holographic Modeling (HHM) approach to structurally and
systematically identify, filter, rank, and evaluate the risks and crises for the gaming industry in Macau.
Through a series of focus group meetings supplemented by four rounds of Delphi questionnaires, 18 risk
factors are identified. The MDS together with the correlation coefficients among the18 risks produce a
Risks Interconnection Map (RIM) to clearly depict the relative positions and association among the 18 risk
factors. The research findings would provide a useful reference for the stakeholders in the industry.
Key words: Hierarchical Holographic Modeling (HHM); Delphi questionnaires; Multi-Dimensional
Scaling (MDS); Risks Interconnection Map (RIM)
Development of the Gaming Industry in Macau
The gaming tax has been the principal income of Macau since licensed gaming began in 1847. First
monopoly on casino was granted to Tai Hing Company in 1934. The casino monopoly franchise was
changed to STDM in 1962 until 2001. In 2001, the Macau government terminated the monopolistic
environment and planned to give out three licenses to increase competition. Twenty-one bids were
received and, in February 2002, three were granted the gaming concession, namely the Sociedade de
Jogos de Macau (SJM), Galaxy Casino (Galaxy) and Wynn Resorts Macau (Wynn). SJM signed an 18-
2 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
year concession valid through March 31, 2020. SJM is 80% owned by STDM, which is 15.8% owned by
Shun Tak and 30% owned by Stanley Ho. Wynn signed a 20-year concession valid through June 26, 2022.
US-listed Wynn Resorts, Ltd owns 90% of the vote and 100% of the economic interest in Wynn. In
March 2006, Wynn sold its gaming license sub-concession to Publishing and Broadcasting Limited (PBL)
of Australia for US$900 million. Galaxy also signed a 20-year concession valid through June 26, 2022. In
December 2002, Galaxy granted a sub-concession to Venetian Macau S.A. (Sands), a wholly owned
subsidiary of US-listed Las Vegas Sands Corp, which runs Venetian Las Vegas. The market share among
the six gaming operators (Macao Daily Times, 2012 February 1) are: SJM 27%; Galaxy Entertainment
Group 19%; Sands China Ltd. 19%; Melco Crown 13%; Wynn Macau 12%, and MGM China 10%.
The growth of the industry has been moderate until its liberalization in 2004 and since then the growth
has been very rapid. The gaming tables have grown from 340 to 5498 in July 2012 (Macao Daily News,
2012 July 17). The slot machine number has increased from around 1,000 to over 17,000 during the
period. The casino revenues in Macau first overtook that of Las Vegas Strip in 2007, and those of Nevada
and New Jersey combined in 2009. The gaming revenues of the largest operator SJM alone have already
exceeded those of some 40 casinos in Las Vegas Strip in 2010 (Hong Kong Commercial Daily, 2011 May
13). In the 2012 policy address, the Macau Chief Executive Fernando Chui Sai On mentioned that while
strengthening and deepening the stable development of the tourism and gaming industries, the
government is moderately adjusting the growth of gaming industry. Stronger supervision of the gaming
industry develops and upgrades integrated tourism relevant industries and promotes a diversified
economy. The government has announced a maximum number of gaming tables, set at 5,500 within three
years beginning in 2010, and strictly controls new casinos and gaming tables. The relevant bureaus also
strictly regulate commission related to junkets (this cannot exceed 1.25% of the betting amount), and
continues to strengthen video surveillance in casinos. By the end of 2012, the government will complete
the auditing works of cage procedures operated by the six gaming concessionaires, and will encourage
them to build internal surveillance systems in accordance to their own needs. In addition, the government
will look to revise minimum internal surveillance requirements and strengthen financial audits in the six
gaming concessionaires. The government continues to study the sports gambling market and will create a
working team in 2012 to implement relevant plans. The government continues to develop responsible
gaming (Jornal Do Cidadao, 2011 November 16).
Risk
Risk can be viewed qualitatively as the result of a threat with adverse effects to a vulnerable system.
3 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Quantitatively, risk is a measure of the probability and severity of adverse effects. Risk is either a
condition of, or a measure of, exposure to misfortune - more concretely, exposure to unpredictable losses.
Risk has three distinct aspects related to the anticipated values of unpredictable losses. The three facets are
Expected Loss, Variability of Loss Values, and Uncertainty about the Accuracy of Mental Models intended
to predict losses (Bostrom and Cirkovic, 2008; Chittester and Haimes, 2004). Risk is the potential of a
disaster occurring and is measured by how likely this is to happen and how badly it will hurt (Wallace and
Webber, 2004). In this study, a gaming industry risk is operationally defined as any event that will affect
the long term, healthy, and harmonious development of the entire gaming industry. Risk is a creeping
event and crisis refers to event with lower occurrence probability but more severe consequence e.g. war,
racial killing, collapse of major infrastructure building, or natural catastrophe.
Molak (1997) suggests that risk assessment is a process whereby the nature and size of a risk are assessed
and characterized. Risk management is a process whereby the ways in which a risk may be abated or
eliminated, or its consequences mitigated, are developed, and appropriate ways are chosen and
implemented. A Key Risk Indicator (KRI) is a measure to indicate the potential presence, level, or trend of
a risk. A KRI indicates whether a risk has occurred or is emerging, a sense of the level of the risk exposure,
the trending of and/or changes in risk exposure. KRIs can provide information about a risk situation that
may or may not exist and serves as a signal for further action. When developed and implemented properly,
KRIs can provide significant insight into changes in the risk profile and bring strategic and operational
value to an organization (Fraser and Simkins, 2010). A systemic risk (i.e. crisis) is the potential loss or
damage to an entire system as contrasted with the loss to a single unit of that system. Systemic risks are
exacerbated by interdependencies among the units often because of weak links in the system. These risks
can be triggered by sudden events or built up over time with the impact often being large and possibly
catastrophic (World Economic Forum, 2010).
The risk-management process involves the following key steps (Alizadeh and Nomikos, 2009):
1. Identification of all significant risks affecting the value of the company (risk identification);
2. Evaluation of the potential frequency and severity of losses due to those risks (risk evaluation);
3. Development and implementation of appropriate methods for the management of the risks (risk
management);
4. Monitoring the performance and suitability of the risk management methods and strategies on an
ongoing basis (risk monitoring)
4 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
According to Chittester and Haimes (2004), risk assessment and management is a process that builds on
two sets of triplet questions. In risk assessment, the following questions are addressed:
-What can go wrong?
-What is the likelihood that it would go wrong?
-What are the consequences?
Answers to these three questions help identify, measure, quantify, and evaluate risks and their
consequences and impacts. Risk management builds on the risk assessment process by answering the
following three questions:
-What can be done and what options are available?
- What are the associated trade-offs in terms of all costs, benefits, and risks?
-What are the impacts of current management decisions on future options?
Hierarchical Holographic Modeling (HHM)
HHM is a holistic methodology aimed at representing the essence of the inherent diverse attributes of a
system - its multiple aspects, perspectives, facets, views, dimensions, and hierarchies. The term
holographic refers to the approach of a multi-view image of a system when identifying vulnerabilities.
Views of risk can include (1) economic, (2) health, (3) technical, (4) political, and (5) social systems. In
addition, risks can be geography related and time related. The term hierarchical refers to the desire to
understand what can go wrong at many different levels of the system hierarchy. HHM recognizes that for
the risk assessment to be complete, there are macroscopic risks that are understood at the upper
management level of an organization that are very different from the microscopic risks observed at lower
levels (Chittester and Haimes, 2004; Borstrom and Cirkovic, 2008). The HHM methodology generates a
comprehensive set of sources of risk, i.e., categories of risk scenarios. These sources are discriminated as
to the likelihood and severity of their consequences, systematically on the basis of principled criteria and
sound premises. For this purpose, the proposed methodological framework for risk filtering and ranking is
based on the following considerations:
• It is often impractical to apply quantitative risk analysis to hundreds of sources of risk. Qualitative
risk analysis may be adequate for decision purposes under certain conditions.
• All sources of evidence should be harnessed in the filtering and ranking process to assess the
significance of the risk sources. Such evidence includes common sense, professional experience,
expert knowledge, and statistical data.
Risk assessment covers risk identification, quantification, and measurement while risk management deals
5 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
with the creative identification and meaningful evaluation of risk mitigation options to address the risks
effectively. The Risk Filtering, Ranking, and Management (RFRM) method involves eight phases
(Horowitz and Haimes, 2003; Haimes, Lambert, Kaplan, Pikus, Leung, 2002; Haimes and Weiner, 1986):
Phase I. Scenario Identification - a Hierarchical Holographic Modeling is developed to describe the as-
planned scenario
Phase II. Scenario Filtering - the risk scenarios identified in Phase I are filtered according to the
responsibilities and knowledge of the domain experts
Phase III. Bi-Criteria Filtering and Ranking - based on likelihood and consequences
Phase IV. Multi-Criteria Evaluation – based on criteria of risk
Phase V. Quantitative Ranking - the filtering and ranking of scenarios continues based on quantitative
and qualitative matrix scales of likelihood and consequence, as well as ordinal-scale response to
scenario resiliency, robustness, and redundancy
Phase VI. Risk Management - intelligence collection options for dealing with the filtered scenarios are
identified, and the cost and the potential for prevention for each event are estimated.
Phase VII. Safeguarding Against Missing Critical Items - scenarios previously filtered out in Phases II
to V are re-examined and compared to the consequences, cost, and preventive potential of the
selected options
Phase VIII. Operational Feedback - experience and information gained during this application are used
to refine the scenario filtering and decision processes in earlier phases.
A very large number of risk scenarios, hierarchically organized into sets and subsets, are generated
through HHM. The RFRM then ranks the elements of the scenario model, giving strong preference to
those elements that are considered most important from several different areas of expertise (Horowitz and
Haimes, 2003). In this paper, only the first five phases of the RFRM method are used. Phases VI to VIII
involve the implementation of the risk management and feedback and are outside the scope of this study.
Research approaches
Owing to the uniqueness of the gaming industry, judgmental sampling approach was adopted. Samples
are drawn based on some criteria which are appropriate for the study. The criteria used in this study
include: education level, place of residence, and type of career. The group of respondents that participated
in the Delphi method and focus groups are either academia with doctorate degree or professors in various
universities in Macau, Hong Kong and China or they hold at least five years of managerial positions in
the gaming industry. The other groups of respondents are Macau residents pursuing university education.
They were selected because of their knowledge about Macau’s gaming industry. A total of 236 sets of
6 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
risk questionnaires were collected and 253 sets of MDS questionnaires were collected.
A core working team comprising of risk management experts from Macau and Hong Kong and industry
veterans was formed in July, 2011 to design, implement and monitor the entire research process. Four
rounds of focus group discussions and numerous working meetings among the core working team
members were held in Macau, Guangzhou, and Hong Kong. The qualitative comments from the focus
groups were analyzed and the findings were constantly cross-validated and triangulated with the
quantitative Risk questionnaire (Appendix 1 is the final version after four rounds of Delphi questionnaire
reaching near consensus view among the experts in the core team). The second questionnaire was
designed to adopt Multi- Dimensional Scaling technique to plot the relative perceptual map of the final
18 risk factors among the respondents (Appendix 2). Initially 20 risk factors were preliminarily identified
through various focus group meetings using the RFRM Method. The Delphi Risk questionnaires were
sent to academic experts, gaming practitioners, and representatives from NGOs (a total of 55 experts and
32 veterans participated). Academic experts are invariably doctoral degree holders or professors in
universities in Macau, Hong Kong, and China. Industry veterans have at least over five years of
managerial experience in the gaming and/or NGO businesses. Four rounds of Delphi Risk questionnaires
were distributed and after consolidating a series of their findings, the questionnaire was gradually revised
to 18 risk factors (see Appendix 1).
Five dimensions of risk are used to measure the risks, viz., severity, probability, detectability, the product
of severity and probability, and the product of severity, probability, and detectability (i.e. the Risk Priority
Number, RPN). The participants comprise of three unique groups, i.e. the academic, veteran professional
casino employees, and university students in Macau. Two sets of questionnaires were administered. One is
called the risk questionnaire which measures the perceived extent of severity, probability, and detectability
of the 18 risks by a total of 236 respondents. The other questionnaire aims to collect the perceived ranking
scores of relative similarity and dissimilarity of the same 18 risks to generate various perceptual maps
among 253 respondents using Multi-Dimensional Scaling (MDS) technique. The MDS together with the
correlation coefficients among the18 risks produce a Risks Interconnection Map (RIM) to clearly depict
the relative positions and association among the 18 risk factors. Five basic risk categories are revealed
from the MDS analysis, viz.: Human Resources; Macau Government Policy; Crises; Customer Source
from China; and Commercial Issues. These 18 risk factors are further studied by exploratory factor
analysis using the five dimensions respectively.
7 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Most risk analysts quantify the severity and probability of risk in the assessment of risks. In this study the
core research team agreed the inclusion of a third dimension of risk quantification, i.e. detectability as
used in Failure Modes and Effect Analysis (FMEA). Failure Modes and Effects Analysis (FMEA) as a
quality management method used in risk assessment in product design, is a systematic group of activities
intended to do three things: (a) recognize and evaluate the potential failures of a product or process and
the effects of those failures, (b) identify actions that could eliminate or reduce the chance of potential
failures occurring, and (c) document the entire process (Meyer, 2000; Crane and Crane, 2006). FMEA is
used to predict and manage risks for products and it is used to quantify more systematically the true extent
of external risks. Using the FMEA method, the extent of perceived external threats (i.e. risks) can be
estimated by use of Risk Priority Numbers (RPN) which can take a value from 1 to 1000 (Each of Severity
(SEV), Probability of Occurrence (OCC) and Detectability (DET) below can have a value from 1 to 10).
The higher is the value of RPN, the more serious the threat is to the organization (Koo, et al., 2011; Koo,
2011).
Risk Priority Numbers (RPN)
= Severity x Probability of Occurrence x Likelihood of detection
� Severity (SEV) indicates how significant the impact of the effect is
� Probability of Occurrence (OCC) indicates how often the cause of the failure mode is to
occur
� Likelihood of Detection (DET) indicates how likely the current control is able to detect the
failure mode
Multi-Dimensional Scaling (MDS) Technique
The origin of MDS was from psychometric. It has a wide range of applications in analyzing data with
proximity or dissimilarity. MDS can describe the structure of a group of items through the distance data
among respective individual pairs of items. Each item is represented by a point in a multi-dimensional
space. Two similar items are represented by two near points and two dissimilar events are represented by
two distant points in the space. Generally speaking, this is the Euclidean two or three dimensional distance.
The Euclidean distance of two points i and j can be represented by the following formula:
dij = [Σ( xia - xja ) 2 ] 1/2
where xia is the coordinate of point i in a dimension
and xja is the coordinate of point j in a dimension.
8 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
From a distance matrix table with continuous data, we can use MDS to reproduce a map showing the
relative positions of the various points. Analogy can be made with that of the works done by a surveyor
who draws up a map representing the series of points whose distances have been surveyed. To start with a
geographic map, one can easily prepare a distance matrix table. However to draw a map from a distance
matrix table would be almost an impossible task. MDS is a scientific statistical tool which can help solve
this particular problem. It can be used to analyze the relationships among the distance data to develop the
spatial map. The geographic map drawing example utilizes continuous data on a symmetric matrix. This
model is called Classical MDS (for one single dissimilarity matrix). In the case of measuring the various
similarities and differences of different items, especially subjective perceptions towards risk factors, it is
not possible to be very exact. Instead the ranking order of risk factors is used to represent the extent of
similarity and difference among them. In this respect the data input form would be an asymmetrical
matrix with ordinal data (i.e. the ranking order in Appendix 2). The MDS positioning technique can be
applied in market segmentation (Koo, H., 2005). In this study MDS is deployed to graphically represent
the relative perceived similarity and difference among the risk factors. The goal of MDS is to detect
meaningful underlying dimensions that explain observed similarities or dissimilarities (distances) between
the investigated objects. It is used in this study to construct the Risks Interconnection Map (RIM).
Risk Analyses for the Gaming Industry in Macau
The following tables depict the demographic pattern of the respondents in the two questionnaire surveys.
A visual comparison of valid percentage distributions indicates that the two samples are rather similar.
Although the sampling approach is of non-probability nature, it should be representative of an educated
or experienced group of respondents who are knowledgeable about the gaming industry in Macau. While
the two groups are not identical, over 85% of them participated in both questionnaire surveys.
Table 1: Distribution of gender of respondents
Risk questionnaire MDS questionnaire
Frequency Valid
percent
Frequency Valid
percent
1 Male 103 45.4 108 47.0
2 Female 124 54.6 122 53.0
Missing 9 23
Total 236 253
9 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
The sample comprises of slightly more female than males. Independent samples T-test can be performed
to discern whether there are significant differences between the two respondent gender groups.
10 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Table 2: Distribution of whether respondents are working in casino
Risk questionnaire MDS questionnaire
Freque
ncy
Valid
percent
Frequency Valid
percent
1 Non casino 136 59.9 132 57.4
2 Casino 91 40.1 98 42.6
Missing 9 23
Total 236 253
About 40% of the respondents are working in the casino industry. It would be useful and interesting to
find out whether the two respondent groups (i.e. those working in the gaming industry and those that are
not) have significantly different views towards the various aspects of risk factors.
Table 3: Descending order of risk severity
11 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
N
Mea
n
Sev15 Revision of FIT policy 236 7.47
Sev14 Sudden economic recession 236 7.37
Sev11 Fierce competition 236 7.25
Sev4 Inadequate HR supply 236 6.94
Sev18 Pandemic disease 236 6.92
Sev9 Corruption issues 235 6.82
Sev7 Irregular funding source disappearing 236 6.76
Sev6 Mono source of customers 236 6.75
Sev8 Improper industry supervision 236 6.73
Sev13 Foreign power domineering 236 6.66
Sev17 Social disorder 236 6.64
Sev10 Biased gaming policy 236 6.63
Sev3 Deterioration of HR quality 236 6.61
Sev2 Shrinking of VIP market 236 6.59
Sev16 Terrorism 235 6.59
Sev1 Neighbor areas liberalize gaming 236 6.38
Sev12 Online gaming gaining popularity 236 6.16
Sev5 Less non-local intermediaries 235 5.68
Valid N (listwise) 233
From Table 3 above, all risk factors are perceived to be severe i.e. with mean score exceeding 5.5.
However the following risk factors are perceived to be more severe on a relative basis:
� Revision of FIT policy
� Sudden economic recession
� Fierce competition
12 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
� Inadequate HR supply
� Pandemic disease
� Corruption issues
The casino industry (i.e. the six gaming operators, Macau Government, gaming intermediaries, and
gaming related institutions) should exercise concerted effort to mitigate the severity of adverse impact
arising from the above six risk factors.
Table 4: Descending order of risk probability
N Mean
Prob11 Fierce competition 236 6.67
Prob4 Inadequate HR supply 236 6.61
Prob1 Neighbor areas liberalize gaming 236 6.50
Prob6 Mono source of customers 236 6.38
Prob9 Corruption issues 236 6.27
Prob12 Online gaming gaining popularity 236 6.09
Prob3 Deterioration of HR quality 236 6.07
Prob10 Biased gaming policy 235 5.85
Prob8 Improper industry supervision 236 5.73
Prob15 Revision of FIT policy 236 5.73
Prob14 Sudden economic recession 236 5.66
Prob7 Irregular funding source disappearing 236 5.45
Prob2 Shrinking of VIP market 236 5.30
Prob13 Foreign power domineering 236 5.24
Prob5 Less non-local intermediaries 236 5.05
Prob17 Social disorder 236 4.86
Prob18 Pandemic disease 236 4.17
Prob16 Terrorism 235 2.94
Valid N (listwise) 234
The following five risk factors are perceived to be more probable to occur on a relative basis:
� Fierce competition
� Inadequate HR supply
� Neighbor areas liberalize gaming
� Mono source of customers
� Corruption issues
13 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Similarly, the stakeholders in gaming industry should work together to avoid the five risk factors from
happening. Fierce competition refers to cut-throat price competition in the form of rebate to customers.
Fierce competition occurred once in 2008 when many casinos began increasing their rebate margin to
attract customers. This unhealthy situation resulted in the Macau Government to intervene by setting the
ceiling of 1.25% rebates for all casino operators. To some extent, this measure helped reduce the
likelihood of fierce competition from occurring.
Table 5: Descending order of risk detectability
N Mean
Det16 Terrorism 235 6.96
Det18 Pandemic disease 236 6.82
Det14 Sudden economic recession 236 5.95
Det9 Corruption issues 236 5.76
Det15 Revision of FIT policy 234 5.71
Det7 Irregular funding source disappearing 236 5.69
Det17 Social disorder 236 5.58
Det8 Improper industry supervision 236 5.38
Det13 Foreign power domineering 236 5.09
Det5 Less non-local intermediaries 236 5.03
Det10 Biased gaming policy 236 4.99
Det11 Fierce competition 236 4.78
Det6 Mono source of customers 236 4.77
Det12 Online gaming gaining popularity 236 4.71
Det3 Deterioration of HR quality 236 4.70
Det2 Shrinking of VIP market 236 4.65
Det4 Inadequate HR supply 236 4.56
Det1 Neighbor areas liberalize gaming 235 3.90
Valid N (listwise) 232
Detectability is operationally defined as the extent of ease to detect, predict, and control the risk. In this
study a Likert scale of risk detectability is used with “1” representing absolutely easy to detect, … , “10”
representing absolutely difficult to detect. Thus a mean score of 5.5 represent a neutral mean score. From
Table 5 above, seven risk factors are perceived to be difficult to detect. However the appearance of
14 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
following risk factors is perceived to be more difficult to detect on a relative basis:
� Terrorism
� Pandemic disease
� Sudden economic recession
� Corruption issues
� Revision of FIT policy
� Irregular funding source disappearing
� Social disorder
Detectability is a complicated concept involving the mixed notion of detecting, predicting, and controlling.
The gaming industry should develop a monitoring scheme to detect, predict, and control the above seven
risk factors.
Table 6: Descending order of risk severity*probability
N Mean
SP11 Fierce competition 236 50.8347
SP4 Inadequate HR supply 236 49.0127
SP6 Mono source of customers 236 45.3644
SP9 Corruption issues 235 44.5745
SP15 Revision of FIT policy 236 44.2500
SP14 Sudden economic recession 236 43.2288
SP1 Neighbor areas liberalize gaming 236 42.7966
SP3 Deterioration of HR quality 236 42.5636
SP10 Biased gaming policy 235 40.3787
SP8 Improper industry supervision 236 40.3390
SP12 Online gaming gaining popularity 236 39.9110
SP7 Irregular funding source disappearing 236 37.5636
SP13 Foreign power domineering 236 37.1483
SP2 Shrinking of VIP market 236 36.2797
SP17 Social disorder 236 33.9110
SP5 Less non-local intermediaries 235 30.4468
SP18 Pandemic disease 236 30.2203
SP16 Terrorism 234 19.7350
Valid N (listwise) 231
If the neutral values for severity and probability are 5.5, then the neutral values for the product of the two
risk dimensions would be 30.25. Severity and Probability are two common risk dimensions used to
15 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
quantify the risks. Their product is also a popular risk quantification approach. From Table 6, it can be
observed that 16 out of 18 risks are above the neutral value of 30.25.
Table 7: Descending order of risk priority number (RPN)
N Mean
RPN9 Corruption issues 235 273.3064
RPN14 Sudden economic recession 236 260.1907
RPN15 Revision of FIT policy 234 257.5897
RPN11 Fierce competition 236 249.0678
RPN8 Improper industry supervision 236 231.3941
RPN7 Irregular funding source
disappearing
236 223.3729
RPN4 Inadequate HR supply 236 223.2458
RPN6 Mono source of customers 236 208.6737
RPN10 Biased gaming policy 235 206.4596
RPN18 Pandemic disease 236 205.9746
RPN3 Deterioration of HR quality 236 200.7500
RPN12 Online gaming gaining popularity 236 195.2034
RPN13 Foreign power domineering 236 195.0381
RPN17 Social disorder 236 193.4619
RPN1 Neighbor areas liberalize gaming 235 175.5319
RPN2 Shrinking of VIP market 236 173.5975
RPN5 Less non-local intermediaries 235 159.7191
RPN16 Terrorism 233 134.8026
Valid N (listwise) 227
The adoption of Risk Priority Number (RPN) deploys more information of various risks in their
quantification. RPN represents the product of Severity, Probability, and Detectability. Using the means of
neutral value of 5.5 for each risk dimension, the neutral value of RPN should be (5.5)3 = 166.375. A total
of 16 risk factors are perceived to be worth monitoring after aggregating the impact of the three
dimensions of risk factors (i.e. severity, probability, and detectability). The risk factor of corruption issues
came top on the list. The six gaming operators together with the Macau Government should set up a more
effective anti-corruption scheme to combat this risk factor.
Table 8: Independent Samples T-Test with Gender as independent variable
16 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
D1
Gender N Mean
Std.
Deviati
on
Std.
Error
Mean
Sev18 Pandemic
disease
1 Male 103 6.47 3.096 .305
2 Female 124 7.23 2.589 .232
Prob16 Terrorism 1 Male 103 2.40 1.844 .182
2 Female 123 3.40 2.242 .202
SP16 Terrorism 1 Male 102 15.137 15.4868 1.5334
2 Female 123 23.707 19.7980 1.7851
SP18 Pandemic
disease
1 Male 103 26.786 22.2219 2.1896
2 Female 124 32.871 22.2703 1.9999
RPN16 Terrorism 1 Male 102 92.892 86.7780 8.5923
2 Female 122 171.02 169.651 15.359
Inferential statistical analyses are performed to identify which demographic variables are of discerning
nature. In this respect, the views from each respondent group can be better understood. In reading Table
8, prefixes “Sev” denoting “Severity dimension, “Prob” denoting “probability of occurrence”, “SP”
denoting “The product of Severity and Probability of Occurrence”; and “RPN” denoting “Risk Priority
Number”. On the whole female respondents are significantly (at 0.05 level) more sensitive to the risks of
Pandemic disease and Terrorism than the male respondents. This may be due to female’s more gentle and
humane character than their male counterpart.
17 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Table 9: Independent Samples T-Test with Whether working in casinos as independent variable
D3 Working in
Casino? N Mean Std. Deviation
Std. Error
Mean
Sev2 Shrinking of VIP
market
1 Non casino 136 6.81 2.216 .190
2 Casino 91 6.18 2.283 .239
Sev10 Biased gaming
policy
1 Non casino 136 6.88 2.112 .181
2 Casino 91 6.25 2.025 .212
Sev16 Terrorism 1 Non casino 136 7.07 3.205 .275
2 Casino 90 5.79 3.384 .357
Sev18 Pandemic disease 1 Non casino 136 7.21 2.717 .233
2 Casino 91 6.41 2.989 .313
Prob4 Inadequate HR
supply
1 Non casino 136 6.29 2.360 .202
2 Casino 91 7.10 2.441 .256
Prob6 Mono source of
customers
1 Non casino 136 6.12 2.251 .193
2 Casino 91 6.79 2.229 .234
Prob10 Biased gaming
policy
1 Non casino 135 5.59 2.211 .190
2 Casino 91 6.24 2.120 .222
Prob11 Fierce competition 1 Non casino 136 6.40 2.426 .208
2 Casino 91 7.05 2.147 .225
SP4 Inadequate HR
supply
1 Non casino 136 45.6324 25.92034 2.22265
2 Casino 91 53.9121 28.98530 3.03849
SP6 Mono source of
customers
1 Non casino 136 41.2206 25.07166 2.14988
2 Casino 91 51.0549 27.78303 2.91245
The perception between those working in casinos and those who are not are somewhat mixed. As far as
risk severity is concerned, those who are not in the gambling industry are significantly (at 0.05 level)
more sensitive in shrinking of VIP market; Biased gaming policy; Terrorism, and Pandemic disease.
Those working in the casinos have significantly (at 0.05 level) higher perception of probability of
occurrence of the following risks: Inadequate HR supply, Mono source of customers, Biased gaming
policy, and Fierce competition. They are also more concerned with Inadequate HR supply and Mono
supply of customers when both severity and probability are considered together. In short those not
working in the gaming industry perceived the severity of some risk factors significantly higher than those
who work in the casinos. On the other hand those working in the casinos feel the probability of certain
18 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
risk factors significantly higher than those who are not working in the casinos.
Table 10: Independent Samples T-Test with Marital Status as independent variable
D2 Marital
Status N Mean Std. Deviation
Std. Error
Mean
Sev3 Deterioration of HR
quality
1 Married 28 5.82 2.667 .504
2 Not Married 198 6.76 2.137 .152
Sev4 Inadequate HR
supply
1 Married 28 5.82 2.868 .542
2 Not Married 198 7.09 2.230 .158
Sev5 Less non-local
intermediaries
1 Married 28 4.43 2.150 .406
2 Not Married 197 5.78 2.173 .155
Sev16 Terrorism 1 Married 28 5.32 3.507 .663
2 Not Married 197 6.75 3.272 .233
Det9 Corruption issues 1 Married 28 4.79 2.587 .489
2 Not Married 198 5.91 2.527 .180
Det18 Pandemic disease 1 Married 28 7.89 2.424 .458
2 Not Married 198 6.69 3.114 .221
SP5 Less non-local
intermediaries
1 Married 28 21.6071 17.54009 3.31477
2 Not Married 197 30.6244 19.50199 1.38946
SP16 Terrorism 1 Married 28 13.2143 12.18486 2.30272
2 Not Married 196 20.8214 19.02694 1.35907
RPN4 Inadequate HR
supply
1 Married 28 148.4286 121.19877 22.90442
2 Not Married 198 236.9848 199.57810 14.18340
RPN5 Less non-local
intermediaries
1 Married 28 99.1786 80.82723 15.27491
2 Not Married 197 165.2843 148.10590 10.55211
RPN9 Corruption issues 1 Married 27 188.0370 181.00733 34.83488
2 Not Married 198 285.2778 238.44830 16.94578
On the whole those who are not married are more sensitive to the following risks: Deterioration of HR
quality, Inadequate HR supply; less non-local intermediary; Terrorism; Corruption issues; Pandemic
disease; and Corruption issues. Being single, they have less family burden and are more cautious about
the future of the casino industry.
19 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
20 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Table 11: Independent Samples T-Test with Age in casinos as independent variable
D4
Age N Mean
Std.
Deviation
Std. Error
Mean
Sev1 Neighbor areas
liberalize gaming
>= 30 26 5.46 2.302 .451
< 30 199 6.49 2.322 .165
Sev16 Terrorism >= 30 26 5.00 3.453 .677
< 30 198 6.79 3.258 .232
Sev17 Social disorder >= 30 26 5.58 2.730 .535
< 30 199 6.72 2.427 .172
Det4 Inadequate HR
supply
>= 30 26 3.42 2.386 .468
< 30 199 4.75 2.412 .171
SP1 Neighbor areas
liberalize gaming
>= 30 26 35.0000 17.24181 3.38140
< 30 199 43.4322 24.68824 1.75010
RPN3 Deterioration of HR
quality
>= 30 26 142.1154 134.61748 26.40066
< 30 199 207.8392 164.91530 11.69054
RPN4 Inadequate HR
supply
>= 30 26 150.5385 190.55587 37.37108
< 30 199 235.8442 192.70955 13.66082
Bulk of the respondents’ ages is below 30 years. The sample is divided into two age groups and
Independent samples T-Test is performed. In general those over 30 years old are more sensitive towards
the risk items.
Table 12: ANOVA with Working Experience as an independent variable
D5 Working
Experience Sev16 Terrorism
Prob4 Inadequate
HR supply
Det5 Less non-local
intermediaries RPN16 Terrorism
1 No 7.39 6.24 5.04 151.6460
2 less than 2 years 5.38 6.78 5.07 63.7308
3 2-5 years 5.79 7.50 5.23 134.9375
4 5-10 years 5.48 6.88 5.32 110.3600
5 Over 10 years 7.38 5.63 2.88 209.3750
Total 6.59 6.63 5.04 135.0182
Significant at 0.05
level
1>2
1>3
1<3 3>5 1>2
21 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Those with no working experience perceive the severity and RPN of Terrorism to be significantly (at 0.05)
higher than those of working less than two years. They perceive the probability of inadequate HR supply
risk to be significantly (at 0.05) less than those who are working for 2-5 years. Those with less than two
years of working experience perceive the RPN of Terrorism to be significantly (at 0.05) less than those
who have no working experience.
Table 13: Summary of all risk factor means grouped under respective Risk Clusters
Risk Cluster Severity(S)Severity(S)Severity(S)Severity(S) Probability(P)Probability(P)Probability(P)Probability(P) Detectability(D)Detectability(D)Detectability(D)Detectability(D) (SP)(SP)(SP)(SP)1/21/21/21/2 (SPD)(SPD)(SPD)(SPD)1/31/31/31/3
Human ResourcesHuman ResourcesHuman ResourcesHuman Resources 3-HR Quality 6.61 6.07 4.7 6.21 5.41
4-HR Supply 6.94 6.61 4.56 6.66 5.59
Cronbach Alpha 0.717 0.697 0.793 0.741 0.76
Macau Government PolicyMacau Government PolicyMacau Government PolicyMacau Government Policy 8-Improper Industry Supervision 6.73 5.73 5.38 6.09 5.67
13-Foreign Power Domineering 6.66 5.24 5.09 5.69 5.23
10-Biased Gaming Policy 6.63 5.85 4.99 6.09 5.48
9-Corruption Issues 6.82 6.27 5.76 6.38 5.99
11-Fierce Competition 7.25 6.67 4.78 6.84 5.82
Cronbach Alpha 0.762 0.728 0.676 0.755 0.742
CrisesCrisesCrisesCrises 17-Social Disorder 6.64 4.86 5.58 5.46 5.27
14-Sudden Economic Recession 7.37 5.66 5.95 6.29 5.94
18-Pandemic disease 6.92 4.17 6.82 5.09 5.32
16-Terrorism 6.59 2.94 6.96 4.02 4.54
Cronbach Alpha 0.812 0.667 0.629 0.711 0.679
Customer Source from ChinaCustomer Source from ChinaCustomer Source from ChinaCustomer Source from China 15-FIT Policy 7.47 5.73 5.71 6.35 5.88
6-Mono Source of Customers 6.75 6.38 4.77 6.41 5.5
Cronbach Alpha 0.402 0.311 0.338 0.374 0.431
Commercial Issues Commercial Issues Commercial Issues Commercial Issues 5-Less Non-Local Local Intermediaries 5.68 5.05 5.03 5.2 4.98
12-On-line gaming gaining popularity 6.16 6.09 4.71 5.95 5.24
2-VIP Market Shrinking 6.59 5.3 4.65 5.77 5.2
1-Neighber Areas Liberalize Gambling 6.38 6.5 3.9 6.24 5.02
7-Irregular Funding Sources disappearing 6.76 5.45 5.69 5.88 5.65
Cronbach Alpha 0.526 526 0.603 0.542 0.568
Table 13 summarizes the perceived magnitudes of the 18 risk factors from five different dimensions. The
last two columns in the table represent the means of the square roots and cube roots of the products of
“severity and probability” and those of “severity, probability and detectability” respectively. These
conversions normalize the range of scores from “1” to “10” as in the original input in order to make them
more comparable. The Cronbach alpha for reliability measure for each of these risk factor categories are
listed in Table 13. The five risk categories are grouped and named through discussions among the subject
experts of the core research team and have under this circumstance some face validity.
22 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Multi-Dimensional Scaling (MDS)
Multidimensional scaling (MDS) is a set of related statistical techniques used in information visualization
for exploring similarities or dissimilarities in data. An MDS algorithm starts with a matrix of item–item
similarities, and then assigns a location to each item in N-dimensional space, where N is specified a priori.
For sufficiently small N, the resulting locations may be displayed in a graph or 3D visualization.
(http://en.wikipedia.org/wiki/Multidimensional_scaling). MDS is used especially in behavioural,
econometric, and social sciences to analyze subjective evaluations of pair wise similarities of entities.
MDS is often used for creating a space where the entities can be represented as vectors, based on some
evaluation of the dissimilarities of the entities (http://users.ics.tkk.fi/sami/thesis/node15.html).
Figure1: MDS of all 253 respondents
Figure 1 depicts a two dimensional perceptual mapping of the 18 risk factors by all 253 respondents.
There are five categories of risks visually present in the map. Dimension 1 of the MDS perceptual map
may be interpreted to denote “Macau Government policy vs. Sources of supply of gamblers” and
23 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Dimension 2 to denote “risk vs. crisis”.
Risks Interconnection Map (RIM)
Figure 2 illustrates the interconnectedness of the risk factors in terms of correlation coefficients of RPN.
Interconnection lines are shown for any pair of risk factors that have correlation coefficients exceeding
0.4 (all are significant at 0.01 levels). Thick lines (with arrow head at the ends of the lines) are drawn for
correlation coefficients exceeding 0.5. The RIM is a succinct and vivid way to depict the
interconnection among the risk factors. Strong correlation coefficients imply a possible causal
relationship.
Figure 2: Risk Interconnection Map (RIM) representing RPN linkages
Risk Priority Number (RPN) is the product of Severity, Probability, and Detectability containing the most
information about risks concerned. “HR inadequacy vs. HR quality” and “corruption vs. improper
supervision” are highly interconnected. The risks of sudden economic recession and pandemic disease are
interconnected (i.e. the nature of change is similar). Biased gaming policy is correlated with corruption
24 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
and fierce competition. Cross cluster interconnectedness exist between HR quality and Neighbor area
liberalizing gaming suggests that employees may be attracted by neighboring areas and causing
deteriorating HR quality. On-line gambling and Foreign influence is interconnected may be due to the
perception by respondents that on-line gambling gaining popularity may trigger foreign power to exercise
more of their influence in Macau to protect their vested interest in Macau.
MDS versus Factor Analysis
Being able to uncover underlying dimensions based on a series of similarity judgments by respondents,
MDS is popular in many research situations. MDS may be thought of as a way of representing subjective
attributes in objective scales. A type of perceptual mapping, the central MDS output is a set of scatter
plots in which the axes are the underlying dimensions and the points are the objects of comparison. The
objective of MDS is to array points in multidimensional space such that the distances separating points
physically on the scatter plot reflect as closely as possible the subjective distances obtained by the
respondents. MDS shows graphically how different objects of comparison do or do not cluster. MDS is
mainly used to compare objects when the bases (i.e. dimensions) of comparison are not known. Though it
is possible to use MDS with quantitative variables, it is more common to use factor analysis to group
variables whose dimensions are objective and measurable. Because MDS does not require assumptions of
linearity, or multivariate normality, sometimes it is preferred over factor analysis
(http://faculty.chass.ncsu.edu/garson/PA765/mds.htm). In factor analysis, the similarities between
variables are expressed in the correlation matrix. With MDS, any kind of similarity or dissimilarity matrix
can be used. MDS and factor analysis are fundamentally different methods. Factor analysis requires that
the underlying data are distributed as multivariate normal, and that the relationships are linear. MDS
imposes no such restrictions. As long as the rank-ordering of similarities in the matrix is meaningful, MDS
can be used. In terms of resultant differences, factor analysis tends to extract more factors (dimensions)
than MDS; as a result, MDS often yields more readily, interpretable solutions. MDS can be based on
respondents' direct assessment of similarities between stimuli, while factor analysis requires subjects to
rate those stimuli on some list of attributes (for which the factor analysis is performed). In summary, MDS
methods are applicable to a wide variety of research designs because distance measures can be obtained in
any number of ways (http://www.statsoft.com/textbook/multidimensional-scaling/ ).
Exploratory Factor Analyses were performed on the five risk dimensions respectively. The results are
shown in Tables 14 to 18. Both KMO measure of sampling adequacy and Bartlett’s test of sphericity
suggest the factorability of the 18 risk items according to the five dimensions. These factor analyses
25 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
provide additional insight of the risk factors that may be useful to the academia and business practitioners
alike.
26 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Table 14: Factor Analysis on Severity of the 18 Risk Factors
Table 15: Factor Analysis on Probability of the 18 Risk Factors
27 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Table 16: Factor Analysis on Detectability of the 18 Risk Factors
28 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Table 17: Factor Analysis on product of Severity and Probability of the 18 Risk Factors
Table 18: Factor Analysis on RPN of the 18 Risk Factors
29 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Conclusion and Implications
The Risk Priority Number (RPN) contains the fullest information (i.e. Severity, Probability, and
Detectability) about the attributes of risk factors and it serves as a single aggregated measure of the
magnitudes of the risks. From Table 7, the following risks need to be addressed with higher degree of
priority by the stakeholders in the gaming industry in Macau. Information provided in Table 3 to table 6
provides supplementary information.
The economic implications of the seven most important risks (with the highest RPN) are as follows:
1) Corruption issues – This risk would jeopardize the image of Macau of being a leading casino city
in the world. The investment costs in Macau’s casino and related industries will be unnecessarily
increased and be less certain. This corruption image may adversely affect future investments into
Macau.
2) Sudden Economic Recession – The world has been badly hit by the financial tsunami since 2008.
The European debt crisis has slowed down the growth of casino business in 2012. The disposable
incomes of most people will decrease in economic recession times and this would affect the
gaming industry as well.
3) Revision of FIT Policy by the Chinese Government – Macau is heavily dependent on tourist and
30 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
patrons coming from China. Being a tourist city with no natural resources, Macau gaming
industry’s prosperity relies very much on the FIT policy which can be adjusted rather quickly.
4) Fierce Competition – Fierce competition through price cutting (i.e. rebates for gaming chips)
affect the long term sustainability of individual casino operators and intermediaries who may
experience higher bad debts.
5) Improper industry supervision – This may lead to image deterioration of the industry as a whole,
affecting Macau gaming industry’s competitiveness in the region. Poor supervision will adversely
affect the quality of the service of the six gaming operators and increase the operating costs of the
casinos.
6) Irregular funding source disappearing – This risk would mainly affect the VIP market in Macau.
Irregular funding provides the credit liquidity and financial channel for VIP gamblers.
7) Inadequate supply of Human Resource – This risks affect the operating costs of the casinos
directly as employee salary is the main expenditure of the employer in the service sector. The
quality of human resources has been deteriorating in recent years. This will affect the service
standard and competitiveness of the gaming industry.
The following are the respective recommendations of the key risks for the gaming industry in Macau:
1) Corruption issues - This may be caused by improper supervision by the Macau Government. The
industry should pressurize the Macau Government to make the gaming policy more transparent.
Fair mechanism in policy making should be adopted.
2) Sudden Economic Recession – The “suddenness” in a major economic recession makes it difficult
to predict (with detectability score mean of 5.95) and control the risk. An econometric predictive
model of relevant economic variables on the casino revenues should be developed to predict and
monitor the effect of economic recession on the gaming industry. It is also important for the
industry to guard against unwarranted and unhealthy expansion of gaming tables. In this respect
the Macau Government has announced that the annual growth rate of gaming tables would be
contained at 3% per annum after 2013. Moreover the industry should contain their costs through
enhancing their cost effectiveness through various quality management initiatives. Individual
casino operators should be required to prepare and maintain a Business Continuity Plan.
3) Revision of FIT Policy by the Chinese Government – The industry collectively should build a
positive image of the gaming industry portraying an image that is in line with the national policy
of turning Macau into a genuine leisure and entertainment tourist attraction. An industry survey in
the form of Customer Satisfaction Index will help enhance the positive image of Macau as a
31 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
tourist city. Once this positive image is created then the FIT policy will be more like to remain
unchanged or even be further relaxed by the Chinese Government. The industry should also
appeal to the Chinese Central Government and the Macau Government to simplify the
immigration procedure and to extend the immigration service to 24 hours a day.
4) Fierce Competition – Competition is unavoidable and can be healthy to the industry as long as
cut-throat price competition is prevented. As far as commission rebate is concerned the Macau
Government has made a clear guide line that it cannot exceed 1.25%. The operators in the casino
should be encouraged to compete on service and quality. This would help establish a positive
image for the gaming industry and also bring positive financial return to the individual operators.
The adoption of Customer Relationship Management technology can help prevent frauds and
cheating against the casinos. This technology can also help detect money laundry activities in the
casinos.
5) Improper industry supervision - The correlation coefficients of “Improper industry supervision”
and “Corruption issues”, are high and significant. Apart from a high degree of interconnectedness,
it cannot conclude which is the cause and which is the effect. The quality of industry supervision
needs improvement. Criteria of an ideal industry supervision should be formulated and then the
quality of gaming industry be properly monitored in a fair and open manner. The criteria of good
industry supervision should perhaps include adjectives like: long term development, healthy
competition; harmonious atmosphere in the society; positive and professional image of the
industry; supervision being fair and open.
6) Irregular funding source disappearing – Over 70% of the gaming revenues come from VIP market.
If the irregular funding is stopped then it would have a major adverse impact on the casino
revenues. The industry operators should build a fair and professional image as an integrated
cultural and entertainment resort area to attract customers and to take effective measures to
prevent money laundry activities in the casinos.
7) Inadequate supply of Human Resource – The Macau Government and the gaming industry should
enhance their effects in training and re-training. A retraining institution should be established to
help Macau residents to adapt to the changing environment. Apart from the corporate social
responsibility requirements, the casino operators can help address the issue of human resource
shortage. Among the gaming operators they should not compete on salary (a different form of
price competition), they should compete in the labour market by being a responsible and caring
employers to recruit and retain their employees. As the future growth on gaming table would be
capped at 3% per annum, the demand on front line dealers would be stabilized and there is
32 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
already a trend that newly established casinos shifted their focus on slot machines and through
closed circuit video technology.
Limitations and Future Research
As risks are dynamic, changing, and difficult to detect, other factors may need to be explored in order to
give a more comprehensive explanation to risk propensity in the gaming industry. Future research may be
extended to explore the remaining phases of the Risk, filtering, Ranking, and Management model to
contribute to a comprehensive gaming industry risk management. Being vigilant in peace time and being
able to think of possible risks and crises while living in a safe environment should be practised by the
stakeholders in the gaming industry.
33 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Appendix 1
Risk Questionnaire
The Macau Gaming Research Association (MGRA) is conducting a study on “Risk and Crisis Analyses for the Gaming Industry in Macau”. This research is funded by the Macao Foundation and the results will be released to the public. We expect the findings will provide useful reference information to the Macau Government, the Gaming Industry, Academic Institutions, and the public. The research tools include Delphi method which collects opinions from selected experts on an anonymous basis and the experts do not meet and discuss among themselves. After several iterations of data collection, analyses, and modifications the views can converge closer to a consensus opinion. These findings will be used as a basis for prediction. The MGRA sincerely invites you as an expert on the gaming industry to complete the questionnaire. We appreciate your support and cooperation and will send you a copy of the findings in due course. Industry risk is any event that will affect the long term, healthy, and harmonious development of the
entire gaming industry. (Risk is operationally defined as a creeping event. Crisis refers to event with
lower occurrence probability but more severe consequence, e.g. war, racial killing, collapses of
major infrastructure building, or natural catastrophe). Severity (represents the extent of negative impact on the gaming industry in Macau, 1=absolutely not severe, … , 10= absolutely severe) Probability (represents the likelihood of the risk or crisis happening, 1=absolutely not likely to happen, … , 10= absolutely likely to happen) Detectability (represents the extent of ease to detect, predict, and control the risk or crisis, 1=absolutely easy to detect, … , 10= absolutely difficult to detect)
Item Factor that might affect the Macau
gaming industry Severity Probability Detectability
1 Neighbor areas liberalize gaming
2 Shrinking of VIP market
3 Deterioration of HR quality
4 Inadequate HR supply
5 Less non-local intermediaries
6 Mono source of customers
7 Irregular funding source disappearing
8 Improper industry supervision
9 Corruption issues
10 Biased gaming policy
11 Fierce competition
12 Online gaming gaining popularity
13 Foreign power domineering
14 Sudden economic recession
34 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
15 Revision of FIT policy
16 Terrorism
17 Social disorder
18 Pandemic disease
Thank you
Appendix 2
MDS Questionnaire
Please compare the following 18 risks line by line. Rank the most similar pair as “1”, the second most
similar pair as “2”, … , the most dissimilar pair as “17”
Det
erio
rati
on
of
HR
qu
alit
y
Irre
gu
lar
fun
din
g s
ou
rce
dis
ap
pea
rin
g
Fore
ign
po
we
r d
om
inee
rin
g
Les
s n
on
-lo
ca
l in
term
ed
iarie
s
Sud
den
eco
no
mic
rec
essi
on
Ter
rori
sm
Rev
isio
n o
f F
IT p
oli
cy
Fie
rce
co
mp
etit
ion
Imp
rope
r in
dust
ry s
up
erv
isio
n
Shr
ink
ing o
f V
IP m
ark
et
Cor
rupti
on
iss
ues
Pan
dem
ic d
isea
se
On
lin
e ga
min
g g
ain
ing
po
pula
rit
y g
ain
ing
pop
ula
rit
y
Mon
o s
our
ce o
f cu
stom
ers
Nei
ghb
or a
reas
libe
rali
ze g
amin
g
Inad
equa
te H
R s
uppl
y
Soc
ial d
iso
rder
Bia
sed
gam
ing
polic
y
Deterioration of HR quality 0
Irregular funding source disappearing 0
Foreign power domineering 0
Less non-local intermediaries 0
Sudden economic recession 0
Terrorism 0
Revision of FIT policy 0
Fierce competition 0
Improper industry supervision 0
Shrinking of VIP market 0
Corruption issues 0
Pandemic disease 0
Online gaming gaining popularity 0
Mono source of customers 0
Neighbor areas liberalize gaming 0
Inadequate HR supply 0
Social disorder 0
Biased gaming policy 0
Personal particulars:
Age: 1- Male[ ] 2- Female[ ]
Whether working in Casino: 1- Not working in Casino [ ] 2- Working on Casino [ ]
Age: 1- 30 years and below [ ] 2- Over 30 years old [ ]
Working Experience: 1- No experience [ ] 2- less than 2 years [ ]
3- 3 to 5 years [ ] 4- 5 to 10 years [ ] 5- Over 10 years [ ]
35 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Appendix 3
Correlation Coefficient matrix of RPN scores of the 18 risk factors
RPN1
Neighbor
areas
liberalize
gaming
RPN2
Shrinking of
VIP market
RPN3
Deterioratio
n of HR
quality
RPN4
Inadequate
HR supply
RPN5 Less
non-local
intermediari
es
RPN6 Mono
source of
customers
RPN7
Irregular
funding
source
disappearin
g
RPN8
Improper
industry
supervision
RPN9
Corruption
issues
RPN10
Biased
gaming
policy
RPN11
Fierce
competition
RPN12
Online
gaming
gaining
popularity
RPN13
Foreign
power
domineerin
g
RPN14
Sudden
economic
recession
RPN15
Revision of
FIT policy
RPN16
Terrorism
RPN17
Social
disorder
RPN18
Pandemic
disease
RPN1 Neighbor areas
liberalize gaming
1 .373**
.475**
.343**
.212**
.185**
.226**
.175** .127 .231
**.236
**.178
**.303
**.218
**.211
** .041 .247**
.162*
RPN2 Shrinking of VIP
market.373** 1 .299** .256** .389** .270** .196** .183** .079 .243** .140* .210** .137* .214** .315** .026 .066 .098
RPN3 Deterioration of
HR quality.475** .299** 1 .550** .299** .201** .286** .317** .261** .321** .299** .326** .324** .328** .307** .061 .352** .130*
RPN4 Inadequate HR
supply.343** .256** .550** 1 .312** .217** .322** .267** .227** .245** .272** .254** .163* .233** .243** .063 .275** .050
RPN5 Less non-local
intermediaries.212
**.389
**.299
**.312
** 1 .274**
.371**
.226**
.198**
.372**
.293**
.289**
.228**
.223**
.251** .059 .107 .020
RPN6 Mono source of
customers.185** .270** .201** .217** .274** 1 .264** .284** .205** .348** .344** .118 .187** .158* .226** -.001 .098 .047
RPN7 Irregular funding
source disappearing.226** .196** .286** .322** .371** .264** 1 .275** .253** .351** .377** .162* .258** .181** .204** .210** .090 .026
RPN8 Improper
industry supervision.175** .183** .317** .267** .226** .284** .275** 1 .547** .459** .320** .267** .316** .147* .169** .047 .256** .161*
RPN9 Corruption
issues
.127 .079 .261**
.227**
.198**
.205**
.253**
.547** 1 .459
**.315
**.162
*.206
**.261
**.181
** .006 .199**
.191**
RPN10 Biased gaming
policy.231** .243** .321** .245** .372** .348** .351** .459** .459** 1 .469** .299** .384** .210** .240** .055 .192** .157*
RPN11 Fierce
competition.236** .140* .299** .272** .293** .344** .377** .320** .315** .469** 1 .322** .289** .242** .204** .074 .227** .165*
RPN12 Online gaming
gaining popularity.178
**.210
**.326
**.254
**.289
** .118 .162*
.267**
.162*
.299**
.322** 1 .425
**.249
**.268
** .115 .288** .059
RPN13 Foreign power
domineering.303** .137* .324** .163* .228** .187** .258** .316** .206** .384** .289** .425** 1 .338** .183** .278** .279** .301**
RPN14 Sudden
economic recession.218** .214** .328** .233** .223** .158* .181** .147* .261** .210** .242** .249** .338** 1 .399** .121 .278** .444**
RPN15 Revision of FIT
policy.211** .315** .307** .243** .251** .226** .204** .169** .181** .240** .204** .268** .183** .399** 1 .068 .266** .308**
RPN16 Terrorism .041 .026 .061 .063 .059 -.001 .210** .047 .006 .055 .074 .115 .278
** .121 .068 1 .230**
.287**
RPN17 Social disorder .247** .066 .352** .275** .107 .098 .090 .256** .199** .192** .227** .288** .279** .278** .266** .230** 1 .346**
RPN18 Pandemic
disease.162* .098 .130* .050 .020 .047 .026 .161* .191** .157* .165* .059 .301** .444** .308** .287** .346** 1
Personal particulars:
Age: 1- Male[ ] 2- Female[ ]
Whether working in Casino: 1- Not working in Casino [ ] 2- Working on Casino [ ]
Age: 1- 30 years and below [ ] 2- Over 30 years old [ ]
Working Experience: 1- No experience [ ] 2- less than 2 years [ ]
3- 3 to 5 years [ ] 4- 5 to 10 years [ ] 5- Over 10 years [ ]
36 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
References:
Alizadeh, A. H., & Nomikos, N. K. (2009). Shipping Derivatives and Risk Management. New York:
Palgrave MacMillan.
Bostrom, N., & Cirkovic, M. M. (Eds.). (2008). Global Catastrophic Risks. New York: Oxford University
Press
Chittester, C. G., & Haimes, Y. Y. (2004). Risks of Terrorism to Information Technology and to Critical
Interdependent Infrastructures Journal of Homeland Security and Emergency Management, 1(4).
Crane, J., & Crane, F. G. (2006). Preventing Medication Errors in Hospitals through a Systems Approach
and Technological Innovation: A Prescription for 2010. Hospital Topics: Research and Perspectives
on Healthcare, 84(4), 3-8.
Fraser, J., & Simkins, B. J. (Eds.). (2010). Enterprise risk management : today's leading research and best
practices for tomorrow's executives. Hoboken, New Jersey: JohnWiley & Sons, Inc.
Haimes, Y. Y., J. H. Lambert, Kaplan, S., Pikus, I., & Leung, F. (2002). A Risk Assessment Methodology
for Critical Transportation Infrastructure. Virginia: Virginia Research Transportation Council.
Haimes, Y. Y., Kaplan, S., & Lambert, J. H. (2002). Risk Filtering, Ranking, and Management
Framework Using Hierarchical Holographic Modeling. Risk Analysis, 22(2), 383-397.
Haimes, Y. Y., and Weiner, A. (1986). Hierarchical Holographic Modeling for Conflict Resolution.
Philosophy of Science, 53(2), 200-222.
Horowitz, B. M., & Haimes, Y. Y. (2003). Risk-Based Methodology for Scenario Tracking, Intelligence
Gathering, and Analysis for Countering Terrorism. Systems Engineering, 6(3), 152-169.
Koo, H. (2005). “A Stratlogic Approach to Review Positioning of Casino Games in Macau” Proceedings
of the International Conference on Gaming Industry & Public Welfare, 8-12 December, Sanya City,
HaiNan Province, China
Koo, L. C. (2011) “Risk and Crises Analyses for the Gaming Industry in Macau” Conference Proceedings
of An International Conference on Public Welfare and Gaming Industry 2011 18-20 October,
Beijing, China, pp. 226-240
Koo, Hannah, Chau, K. Y., Koo, L. C., Liu, Songbai, Tsui, S. C. (2011) A structured SWOT approach to
develop strategies for the government of Macau, SAR Journal of Strategy and Management, Vol. 4
No. 1, 2011 pp. 62-81
Meyer, M. (2000). Risk and failure aspects in twin screw extrusion. Technology, Law and Insurance, 5,
147-153.
Molak, V. (Ed.). (1997). Fundamentals of Risk Analysis and Risk Management. Boca Raton: Lewis
37 Xing, W. X., Koo, L. C., and Khong, Eva Y.W.(2012) “A Critical Review of Risk Factors of the GamingIndustry in Macau” Academy of International Business Southeast Asia Regional Conference 6-8 December, Xiamen, China
Publishers.
Wallace, M., & Webber, L.(2004). The disaster recovery handbook : a step-by-step plan to ensure business
continuity and protect vital operations, facilities, and assets: American Management Association
New York.
World Economic Forum. (2010). Global Risks 2010 A Global Risk Network Report (No. 92-95044-31-2).
Geneva, Switzerland: World Economic Forum.
News :
-, Hong Kong Commercial Daily (13th May 2011) ”Ambrose So: Bring Out Positive Effects Of Gaming
Industry Transform Macao Into A World Tourism and Leisure Center” Page AA1
-, Jornal Do Cidadao (16th November 2011) ”Adjusting The Growth Of Gaming Industry And
Strengthening Supervision” Page 1
-, Macao Daily News (17th July 2012) “MOP74.4 Billion, Gaming Revenue Creates Record High In Q2;
VIP Gaming Business Continues To Drop” Page A10
-, Macao Daily News (1st February 2012) “Citigroup Estimates That Gaming Revenue In January
Increased By Nearly 30%” PageA10
http://en.wikipedia.org/wiki/Multidimensional_scaling
http://faculty.chass.ncsu.edu/garson/PA765/mds.htm
http://users.ics.tkk.fi/sami/thesis/node15.html
http://www.statsoft.com/textbook/multidimensional-scaling/
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